Association between the polymorphisms of matrix metalloproteinases 9 and 3 genes and risk of myocardial infarction in Egyptian patients

Abstract The present study investigated the relationship between the genetic polymorphisms in MMP-9 and MMP-3 genes and acute myocardial infarction (AMI). We examined 40 patients with acute myocardial infarction and 40 age and sex matched controls for MMP-9 functional promoter polymorphism (1562 C > T) and MMP-3 (5A/6A) deletion/insertion polymorphism using restriction fragment length polymorphism (RFLP) for amplified genomic DNA. The frequencies of the combined mutant genotypes CT and TT in the (1562 C > T) MMP9 were significantly higher in AMI patients (20%) when compared to the controls (0%) (p value = 0.005) showing an association between these genotypes and AMI. Also there was a significant difference between 5A/5A genotype and 5A allele frequencies when both are compared in the patients (25% and 35%) and the controls (2.5% and 18.75%) (p= 0.009; OR =13; CI= 1.576–107.233); and (p=0.02; OR =2.333, CI= 1.130–4.820) respectively. In conclusion, the 1562C> T polymorphism of the MMP9 gene is strongly associated with acute myocardial infarction in the Egyptian population. Furthermore, our study supported the presence of the 5A/5A genotype of MMP3 gene promoter polymorphism as a risk factor of AMI in Egyptian patients. Meanwhile, the race selection should be paid more attention since the pathogenesis of a disease might have different bases in different racial population groups.Keywords: Matrix metalloproteinase; 1562C>T; 5A/6A; RFLP; Myocardial infarctionThe Egyptian Journal of Medical Human Genetics (2013) 14, 143–14

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

A Computer-Aided Diagnostic System for Diabetic Retinopathy Based on Local and Global Extracted Features

Diabetic retinopathy (DR) is a major public health problem and the leading cause of vision loss in the working age population. This paper presents a novel deep learning system for the detection and diagnosis of DR using optical coherence tomography (OCT) images. The input for this system is three-channel local and global information from OCT images. The local high-level information is represented by the thickness channel and the reflectivity channel. The global low-level information is represented by the grey-level OCT original image. The deep learning system processes the three-channel input to produce the final DR diagnoses. Experimental results on 200 OCT images, augmented to 800 images, which are collected by the University of Louisville, show high system performance related to other competing methods. Moreover, 10-fold and leave-one-subject-out (LOSO) experiments are performed to confirm how significant using the fused images is in improving the performance of the diagnoses, by investigating four different CNN architectures. All of the four architectures achieve acceptable performance and confirm a significant performance improvement using the fused images. Using LOSO, the best network performance has improved from 90.1 ± 2% using only the grey level dataset to 97.7 ± 0.5% using the proposed fused dataset. These results confirm the promise of using the proposed system for the detection of DR using OCT images